

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 						_A     (				snake_case						,    snake_case						,    snake_case						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      int					=   StableDiffusionInstructPixaPixPipeline
  __lowerCamelCase			:      Optional[int]					=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
  __lowerCamelCase			:      Any					=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  __lowerCamelCase			:      Any					=   IMAGE_TO_IMAGE_IMAGE_PARAMS
  __lowerCamelCase			:      int					=   IMAGE_TO_IMAGE_IMAGE_PARAMS





  def    snake_case_       (  self    ):

      '''simple docstring'''
      torch.manual_seed(0    )
      snake_case							:		Optional[int]        =		UNetaDConditionModel(
          block_out_channels=(32, 64)   ,layers_per_block=2   ,sample_size=32   ,in_channels=8   ,out_channels=4   ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,cross_attention_dim=32   ,)
      snake_case							:		str        =		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_    )
      torch.manual_seed(0    )
      snake_case							:		Optional[int]        =		AutoencoderKL(
          block_out_channels=[32, 64]   ,in_channels=3   ,out_channels=3   ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,latent_channels=4   ,)
      torch.manual_seed(0    )
      snake_case							:		List[str]        =		CLIPTextConfig(
          bos_token_id=0   ,eos_token_id=2   ,hidden_size=32   ,intermediate_size=37   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1000   ,)
      snake_case							:		List[Any]        =		CLIPTextModel(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      snake_case							:		Optional[Any]        =		{
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=0    ):

      '''simple docstring'''
      snake_case							:		int        =		floats_tensor((1, 3, 32, 32)   ,rng=random.Random(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		image.cpu().permute(0   ,2   ,3   ,1    )[0]
      snake_case							:		Any        =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_    )    ).convert("""RGB"""    )
      if str(SCREAMING_SNAKE_CASE_    ).startswith("""mps"""    ):
          snake_case							:		Optional[int]        =		torch.manual_seed(SCREAMING_SNAKE_CASE_    )
      else:
          snake_case							:		Optional[Any]        =		torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		{
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """image_guidance_scale""": 1,
          """output_type""": """numpy""",
      }
      return inputs





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      snake_case							:		Dict        =		self.get_dummy_components()
      snake_case							:		List[Any]        =		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		sd_pipe.to(SCREAMING_SNAKE_CASE_    )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[Any]        =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Union[str, Any]        =		sd_pipe(**SCREAMING_SNAKE_CASE_    ).images
      snake_case							:		int        =		image[0, -3:, -3:, -1]
      assert image.shape == (1, 32, 32, 3)
      snake_case							:		Optional[int]        =		np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      snake_case							:		Tuple        =		self.get_dummy_components()
      snake_case							:		List[Any]        =		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		sd_pipe.to(SCREAMING_SNAKE_CASE_    )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Tuple        =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		"""french fries"""
      snake_case							:		Tuple        =		sd_pipe(**SCREAMING_SNAKE_CASE_   ,negative_prompt=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		output.images
      snake_case							:		List[str]        =		image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      snake_case							:		List[str]        =		np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      snake_case							:		str        =		self.get_dummy_components()
      snake_case							:		Optional[Any]        =		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		sd_pipe.to(SCREAMING_SNAKE_CASE_    )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[int]        =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		[inputs["""prompt"""]] * 2

      snake_case							:		Optional[Any]        =		np.array(inputs["""image"""]    ).astype(np.floataa    ) / 2_55.0
      snake_case							:		Any        =		torch.from_numpy(SCREAMING_SNAKE_CASE_    ).unsqueeze(0    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		image / 2 + 0.5
      snake_case							:		Any        =		image.permute(0   ,3   ,1   ,2    )
      snake_case							:		Optional[int]        =		image.repeat(2   ,1   ,1   ,1    )

      snake_case							:		Tuple        =		sd_pipe(**SCREAMING_SNAKE_CASE_    ).images
      snake_case							:		int        =		image[-1, -3:, -3:, -1]

      assert image.shape == (2, 32, 32, 3)
      snake_case							:		Optional[int]        =		np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      snake_case							:		Union[str, Any]        =		self.get_dummy_components()
      snake_case							:		Optional[int]        =		EulerAncestralDiscreteScheduler(
          beta_start=0.0_00_85   ,beta_end=0.0_12   ,beta_schedule="""scaled_linear"""    )
      snake_case							:		Optional[Any]        =		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		Any        =		sd_pipe.to(SCREAMING_SNAKE_CASE_    )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[int]        =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		sd_pipe(**SCREAMING_SNAKE_CASE_    ).images
      snake_case							:		Optional[int]        =		image[0, -3:, -3:, -1]

      snake_case							:		Union[str, Any]        =		[round(SCREAMING_SNAKE_CASE_   ,4    ) for x in image_slice.flatten().tolist()]
      print(""",""".join([str(SCREAMING_SNAKE_CASE_    ) for x in slice]    )    )

      assert image.shape == (1, 32, 32, 3)
      snake_case							:		Union[str, Any]        =		np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      super().test_inference_batch_single_identical(expected_max_diff=3E-3    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.get_dummy_components()
      snake_case							:		int        =		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_   ,do_normalize=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		pipe.to(SCREAMING_SNAKE_CASE_    )
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		int        =		pipe(**self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_   ,input_image_type="""pt"""    )    )[0]

      snake_case							:		List[str]        =		components["""vae"""]
      snake_case							:		Optional[int]        =		self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_   ,input_image_type="""pt"""    )

      for image_param in self.image_latents_params:
          if image_param in inputs.keys():
              snake_case							:		List[Any]        =		vae.encode(inputs[image_param]    ).latent_dist.mode()

      snake_case							:		Any        =		pipe(**SCREAMING_SNAKE_CASE_    )[0]

      snake_case							:		Tuple        =		np.abs(out - out_latents_inputs    ).max()
      self.assertLess(SCREAMING_SNAKE_CASE_   ,1E-4   ,"""passing latents as image input generate different result from passing image"""    )







@slow
@require_torch_gpu
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_=0    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		torch.manual_seed(SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		load_image(
          """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""    )
      snake_case							:		Union[str, Any]        =		{
          """prompt""": """turn him into a cyborg""",
          """image""": image,
          """generator""": generator,
          """num_inference_steps""": 3,
          """guidance_scale""": 7.5,
          """image_guidance_scale""": 1.0,
          """output_type""": """numpy""",
      }
      return inputs





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,safety_checker=SCREAMING_SNAKE_CASE_    )
      pipe.to(SCREAMING_SNAKE_CASE_    )
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )
      pipe.enable_attention_slicing()

      snake_case							:		Optional[int]        =		self.get_inputs()
      snake_case							:		List[Any]        =		pipe(**SCREAMING_SNAKE_CASE_    ).images
      snake_case							:		str        =		image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 512, 512, 3)
      snake_case							:		Dict        =		np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]    )

      assert np.abs(expected_slice - image_slice    ).max() < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,safety_checker=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Union[str, Any]        =		LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
      pipe.to(SCREAMING_SNAKE_CASE_    )
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )
      pipe.enable_attention_slicing()

      snake_case							:		Union[str, Any]        =		self.get_inputs()
      snake_case							:		Tuple        =		pipe(**SCREAMING_SNAKE_CASE_    ).images
      snake_case							:		Any        =		image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 512, 512, 3)
      snake_case							:		int        =		np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]    )

      assert np.abs(expected_slice - image_slice    ).max() < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,safety_checker=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		DDIMScheduler.from_config(pipe.scheduler.config    )
      pipe.to(SCREAMING_SNAKE_CASE_    )
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )
      pipe.enable_attention_slicing()

      snake_case							:		Optional[Any]        =		self.get_inputs()
      snake_case							:		Union[str, Any]        =		pipe(**SCREAMING_SNAKE_CASE_    ).images
      snake_case							:		Optional[int]        =		image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 512, 512, 3)
      snake_case							:		Any        =		np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]    )

      assert np.abs(expected_slice - image_slice    ).max() < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		0

      def callback_fn(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) -> None:
          snake_case							:		str        =		True
          nonlocal number_of_steps
          number_of_steps += 1
          if step == 1:
              snake_case							:		List[str]        =		latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 64, 64)
              snake_case							:		Union[str, Any]        =		latents[0, -3:, -3:, -1]
              snake_case							:		Dict        =		np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]    )

              assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5E-2
          elif step == 2:
              snake_case							:		Optional[Any]        =		latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 64, 64)
              snake_case							:		Optional[int]        =		latents[0, -3:, -3:, -1]
              snake_case							:		List[str]        =		np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]    )

              assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5E-2

      snake_case							:		List[Any]        =		False

      snake_case							:		Tuple        =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,safety_checker=SCREAMING_SNAKE_CASE_   ,torch_dtype=torch.floataa    )
      snake_case							:		List[str]        =		pipe.to(SCREAMING_SNAKE_CASE_    )
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )
      pipe.enable_attention_slicing()

      snake_case							:		List[Any]        =		self.get_inputs()
      pipe(**SCREAMING_SNAKE_CASE_   ,callback=SCREAMING_SNAKE_CASE_   ,callback_steps=1    )
      assert callback_fn.has_been_called
      assert number_of_steps == 3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      snake_case							:		Dict        =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,safety_checker=SCREAMING_SNAKE_CASE_   ,torch_dtype=torch.floataa    )
      snake_case							:		Tuple        =		pipe.to(SCREAMING_SNAKE_CASE_    )
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )
      pipe.enable_attention_slicing(1    )
      pipe.enable_sequential_cpu_offload()

      snake_case							:		Dict        =		self.get_inputs()
      snake_case							:		int        =		pipe(**SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[Any]        =		torch.cuda.max_memory_allocated()
      # make sure that less than 2.2 GB is allocated
      assert mem_bytes < 2.2 * 10**9





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.get_inputs()
      # resize to resolution that is divisible by 8 but not 16 or 32
      snake_case							:		Any        =		inputs["""image"""].resize((504, 504)    )

      snake_case							:		Union[str, Any]        =		"""timbrooks/instruct-pix2pix"""
      snake_case							:		Optional[Any]        =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
          SCREAMING_SNAKE_CASE_   ,safety_checker=SCREAMING_SNAKE_CASE_   ,)
      pipe.to(SCREAMING_SNAKE_CASE_    )
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )
      pipe.enable_attention_slicing()

      snake_case							:		Union[str, Any]        =		pipe(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		output.images[0]

      snake_case							:		str        =		image[255:258, 383:386, -1]

      assert image.shape == (504, 504, 3)
      snake_case							:		Optional[Any]        =		np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-3










"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}

class 						lowercase   :





      def __init__(			self ,		lowercase=None ,		**lowercase					)						->	int:
            logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""					)
            lowerCAmelCase    		=			model
            lowerCAmelCase    		=			kwargs.get("""model_save_dir""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.get("""latest_model_name""" ,		lowercase					)





      def __call__(			self ,		**lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			{k: np.array(lowercase					) for k, v in kwargs.items()}
            return self.model.run(lowercase ,		lowercase					)





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase=None ,		lowercase=None					)						->	Optional[Any]:
            if provider is None:
                  logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""					)
                  lowerCAmelCase    		=			"""CPUExecutionProvider"""

            return ort.InferenceSession(lowercase ,		providers=[provider] ,		sess_options=lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		**lowercase					)						->	str:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

            lowerCAmelCase    		=			self.model_save_dir.joinpath(self.latest_model_name					)
            lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
            try:
                  shutil.copyfile(lowercase ,		lowercase					)
            except shutil.SameFileError:
                  pass

            # copy external weights (for models >2GB)
            lowerCAmelCase    		=			self.model_save_dir.joinpath(lowercase					)
            if src_path.exists():
                  lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
                  try:
                        shutil.copyfile(lowercase ,		lowercase					)
                  except shutil.SameFileError:
                        pass





      def        _snake_case     (			self ,		lowercase ,		**lowercase ,		)						->	str:
            if os.path.isfile(lowercase					):
                  logger.error(f'Provided path ({save_directory}) should be a directory, not a file'					)
                  return

            os.makedirs(lowercase ,		exist_ok=lowercase					)

            # saving model weights/files
            self._save_pretrained(lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = False ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(lowercase					):
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(
                      os.path.join(lowercase ,		lowercase					) ,		provider=lowercase ,		sess_options=lowercase					)
                  lowerCAmelCase    		=			Path(lowercase					)
            # load model from hub
            else:
                  # download model
                  lowerCAmelCase    		=			hf_hub_download(
                      repo_id=lowercase ,		filename=lowercase ,		use_auth_token=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		)
                  lowerCAmelCase    		=			Path(lowercase					).parent
                  lowerCAmelCase    		=			Path(lowercase					).name
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(lowercase ,		provider=lowercase ,		sess_options=lowercase					)
            return cls(model=lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            lowerCAmelCase    		=			None
            if len(str(lowercase					).split("""@"""					)					) == 2:
                  lowerCAmelCase		,						lowerCAmelCase    		=			model_id.split("""@"""					)

            return cls._from_pretrained(
                model_id=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		use_auth_token=lowercase ,		**lowercase ,		)






